RETRACTED ARTICLE: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection
Abstract Network intrusion detection system (NIDS) can effectively sense network attacks, which is of great significance for maintaining the security of cyberspace. To meet the requirements of efficient and accurate network status monitoring, a NIDS model using Transformer-based fusion deep learning...
Main Authors: | Zelin Xiang, Xuwei Li |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2023-07-01
|
Series: | EURASIP Journal on Wireless Communications and Networking |
Subjects: | |
Online Access: | https://doi.org/10.1186/s13638-023-02279-8 |
Similar Items
-
Network Intrusion Detection Method Based on CNN-BiLSTM-Attention Model
by: Wei Dai, et al.
Published: (2024-01-01) -
Research on sentiment classification for netizens based on the BERT-BiLSTM-TextCNN model
by: Xuchu Jiang, et al.
Published: (2022-06-01) -
Non-Intrusive Air Traffic Control Speech Quality Assessment with ResNet-BiLSTM
by: Yuezhou Wu, et al.
Published: (2023-09-01) -
Predicting Residential Electricity Consumption Using CNN-BiLSTM-SA Neural Networks
by: Meng-Ping Wu, et al.
Published: (2024-01-01) -
CNN-BiLSTM: A Hybrid Deep Learning Approach for Network Intrusion Detection System in Software-Defined Networking With Hybrid Feature Selection
by: Rachid Ben Said, et al.
Published: (2023-01-01)